This workshop provides a hands-on learning experience with a focus on a wider variety of AI tools, their ethical implications and their practical applications. The aim is to facilitate the responsible and efficient use of AI-based tools in research and academia.

Content:

• Understand the importance of using AI in research and academia and assess the benefits and risks involved
• Craft effective prompts for your research tasks
• Develop strategies to integrate AI tools into your research workflow
• Stay informed about and adapt to new developments in the field of AI

At the end of the workshop, you will receive a list of generative AI prompts useful in research and academia. There will be practice sessions during the workshop for which you will need access to AI tools, particularly ChatGPT/GPT-4o. If you do not have an account with ChatGPT/GPT-4o, alternatives like Microsoft Copilot, Google Bard or Claude.ai could also be used.

In the workshop “Fit for AI - Prompting for advanced users” you can get to know and try out prompting tips and techniques. You will learn about prompting techniques such as Few Shot Prompting, Chain-of-Thought Prompting and others. You can try out these techniques directly on various tasks and your own examples.

This seminar focuses on the increasing importance of Artificial Intelligence (AI) in academic research and writing, providing practical insights into AI technologies; use in these areas.The workshop explores ChatGPT and prompt engineering, as well as other academic AI tools to aid research and writing, examining both benefits and challenges. Ethical aspects, such as copyright and authenticity of research results, are discussed, with the goal of equipping participants with practical knowledge and skills to effectively utilize AI in daily research through interactive elements like case studies and group discussions.

The course is practical and aims at teaching students how to:

• Use the programming environment R and RStudio, which includes installation, how to handle errors, problem solve and access helper documents.
• Use basic concepts of programming, such as data types, logical and arithmetic operators, if else conditions, loops and functions.
• Use common R packages to perform basic statistical analysis (e.g., t-test, chi2-test, correlation) and visual presentation (e.g., boxplot, histogram and heat-map) of data in R.

The course is structured with the intent to gradually make students more autonomous in writing code. Starting by introducing a concept through a lecture, then providing formative quizzes and tasks relateed to the concept. This all leads up to a project (exam) where the student gets to combine multiple concepts into a project with the intent of solving a certain problem or displaying specific statistical tests of visual components. 

Do you need to turn data into a publication figure? We offer tools and confidence for the student to independently select a statistical method for research questions in their field. The course is practical and includes implementing a basic statistical analysis in R, the leading statistical programming language in bioinformatics and medical science. Furthermore, we give a brief introduction to visualization in R, with a focus on R/ggplot2. Students can bring data from their own research project, or work on data from the course.

Topics covered include:

• Computational design strategies
• Differential equations
• Programming in Python
• Data analysis

Topics covered include:

• Coding: theory, practical training, coding styles, unit testing
• Collaborative software development workflows
• Data analytics workflows
• (Generalised) linear mixed effects models
• Bayesian statistics
• Data visualisation
• Workflow automation
• Meta-science

Topics covered include:

• Reconstruction of neuron morphologies
• Histological preparation of brain tissue
• Electrophysiological recordings of single neurons in vivo
• Simulations of cellular function via multi-compartmental neuron models

Topics covered include:

• linear and nonlinear time series analysis methods for the characterization of complex dynamical systems
• statistical tools
• analysis of biomedical data (e.g. EEG, structural/functional MRI data)

This course will cover:

• Intro to Jupyter Notebooks, IDEs
• Intro Python (loops, variables, functions)
• Core packages (Numpy, Pandas, Matplotlib, Seaborn)
• Accessing folders (shell, OS)

The module presents a variety of fundamental models and methods from computational neuroscience. By solving daily exercises the students learn how to practically apply the acquired concepts. The course introduces the employed more
advanced mathematical tools embedded into the different topics. Further there will be a pre-course teaching the required programming skills in python.